Overview

Team

SURGE: EPIGAME

Scaffolding of Prediction (And Control Conditions) Will Be Integrated Into
The Game-Play Navigation Interfaces

We propose that navigation interface provides an
excellent opportunity for prediction. Essentially, while real-time and
just-in-time navigation formats are common in games like SURGE: EPIGAME, formats supporting prediction are also not uncommon
and could be developed without breaking the game aspects of SURGE: EPIGAME. Furthermore, a game like
SURGE: EPIGAME provides excellent
opportunities for research on the integration of prediction into games because
of the range of interface formats afforded along a prediction/real-time
continuum. Essentially, real-time or just-in-time navigation formats
engage the player in making decisions during the flow of the level often in a
reflexive manner mirroring constraint-based thinking (e.g., the player
continually micro-adjusts direction and velocity as it becomes apparent that
adjustments are required.

These more predictive interfaces, we hypothesize, should support a higher level
of model-based thinking than constraint based thinking, as well as a higher
percentage of explicit articulation of thinking versus implicit intuitive
thinking that might stay at the level of unaware application of p-prims. This
should be true in terms of comparisons between the Predictive and Real-Time
categories as well as comparisons within the Real-Time category and comparisons
within the Predictive category. We further hypothesize that the GUI variants
within a given sub-category will prove superior to the text-based variants in
terms of engagement, accessibility, and learning. The specific schedule for
conducting these pilot comparisons is outlined later under the research and
development timeline.

Scaffolding of Explanation (And Control Conditions) Will Be Integrated Into
Dialog With Computer Controlled Characters in The Game

We now outline the planned explanation variants
for development and piloting. In addition to the theoretical literature
discussed earlier, we are building on the early design paradigms represented in
Mayer and Johnson (2010), who focused on adding an explanation task following a
feedback event. While playing an electronics quiz-based environment that Mayer
and Johnson defined as being game-like in the sense that a score was kept and
some other core features of games were included, students were tasked with
answering questions posed as circuit diagrams. The research contrast was
whether or not students received immediate feedback on whether their answers
were correct or not, and whether or not students were tasked with providing
self-explanations (chosen from a list of possibly relevant principles) for
their answers. Mayer and Johnson found that the self-explanation alone was more
effective than feedback alone at improving responses on a transfer task, and as
effective as self-explanation combined with feedback. Our interpretation of
these results is that tasking students with some activity that connects the
general scientific principles with the task the student just performed (whether
they were successful or not), could be very conducive for learning in a true
game context, particularly if the explanation was integrated within the fabric
of the game and was more responsive to student ideas. We hypothesize that players in conditions
that include explanation functionality toggled on will outperform players in
null explanation conditions in keeping with the discussed literature on
explanation and the findings of Mayer and Johnson. We hypothesize further,
however, that player-explanation conditions that provide more opportunity for
the player to articulate his or her thinking (i.e., the icon-based
self-explanation and argumentation as explanation variants) will outperform the
fixed text-based self-explanation conditions as well as generate greater
engagement in the game and ideas. Furthermore, we hypothesize that the
player-explanations will outperform the didactic explanations, but that
combining player explanations with didactic explanations, particularly the
reactive didactic explanations, will result in the highest learning gains for
players.